nixtla-model-selector
Automatically selects the best forecasting model between StatsForecast and TimeGPT based on time series data characteristics. Use when unsure which model performs best. Trigger with 'auto-select model', 'choose best model', 'model selection'.
npx skills add majiayu000/claude-skill-registry --skill nixtla-model-selector-intent-solutions-io-plugins-nixtla --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Nixtla Model Selector Automatically selects and executes the optimal forecasting model for time series data. ## Overview This skill analyzes time series data characteristics to determine whether StatsForecast or TimeGPT will yield more accurate forecasts. It evaluates data length, frequency, seasonality, missing values, and series count to make an intelligent selection. The skill then executes the chosen model and returns forecasts with selection rationale. This eliminates manual model selection and experimentation cycles. **Decision Logic**: - **StatsForecast**: Selected for missing values, short data (<30 points), seasonal patterns, or many series (>100) - **TimeGPT**: Selected for long, non-seasonal data with complete observations The skill outputs forecast predictions and a model selection report explaining the decision. ## Prerequisites **Tools**: Read, Write, Bash, Glob, Grep **Environment**: `NIXTLA_TIMEGPT_API_KEY` (required if TimeGPT is selected) **Packages**: ```bash pip install statsforecast nixtla pandas matplotlib statsmodels ``` **Input Format**: CSV file with columns `unique_id`, `ds` (datetime), `y` (target values) ## Instructions ### Step 1: Prepare Data Claude
- Overview
- Prerequisites
- Instructions
- Step 1: Prepare Data
- Step 2: Analyze Data Characteristics
- Step 3: Select Model
- Step 4: Execute Forecast
- Step 5: Generate Output
- Output
- Error Handling
- Examples
- Example 1: Short Seasonal Data
- Example 2: Long Non-Seasonal Data
- Resources
pip install statsforecast nixtla pandas matplotlib statsmodels
What does the nixtla-model-selector skill do?
Automatically selects the best forecasting model between StatsForecast and TimeGPT based on time series data characteristics. Use when unsure which model performs best. Trigger with 'auto-select model', 'choose best model', 'model selection'.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill nixtla-model-selector-intent-solutions-io-plugins-nixtla --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.